Hey friends 👋🏼 Startups are games. This newsletter is about empowering brains (with startup game theory) & the hearts (with intros + celebrations). Today, we’re laying the ground for startup simulations. 😎

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🧮 GTO Moves - The Efficient Customers Hypothesis

Founders play multiple games in parallel, e.g., hiring, distribution, fundraising, PR, among others.

Last week, we properly defined the most important of them that every founder plays - the fundamental game of startups.

In a nutshell, it’s the game where companies compete directly over a finite number of customers.

This week, we will develop it further so that we can start running proper simulations.

The simulations will show an important structure that isn’t really spoken about much at all in the VC/founder literature, but that ultimately every unicorn and market winner has had.

Let’s dive straight in!

1. Recalling the game 👾

Basically, “a multiplayer game where solutions are directly competing over a finite number of customers.”

It’s honestly the same as “Age of Empire” here - you’re both on a map (the specific niche you’re in), competing with other players, over resources (in our case, customers).

🧮 The proper maths

Let C be a customer problem. Consider its associated fundamental game G*. We’re reminding you of the definition below

Every player P_i is competing for a finite pool of M customers. We represent all the M customers using the vector R:=(R_0, R_1,…, R_i) where

  • R_0 is the number of free customers (i.e., customers not using any competing solutions for now)

  • R_i is the current number of customers of solution P_i, for i between 1 to N

2. Quantifying customers’ decision-making 🧠

Now, if we want to run simulations, we need to mathematically flesh out what really makes a customer use a certain solution over another one.

For that, it is fair to assume that customers ultimately only care about 2 things when picking a solution:

  1. How much value they get from picking a specific solution,

  2. How much it costs them to use it.

We break down each of these 2 reasons into 2 further ones, respectively:

  1. Rational value - How much the solution helps them with their problem

  2. Irrational value - How good it makes them feel

  3. Price - The financial cost of using the solution

  4. Switching cost - How time/money expensive is it for them to switch from their current solution to a new one?

These 4 parameters are all well-known & studied reasons that make a customer convert to a solution.

A few illustrative examples:

  1. Example of a superior product → higher conversion: Google vs Yahoo Search

  2. Switching Costs and Customer Loyalty: A Review of Literature

  3. Price Discrimination in the Presence of Customer Loyalty and Differing Firm Costs

🧮 An extensive customer decision-making framework

Practically, when customers are comparing products, they are in reality running an algorithm in their mind.

  1. They assess all solution candidates against each of the above 4 parameters (namely, rational value, irrational value, price, and switching costs).

  2. They then rank them based on an aggregated score that will weight the parameters differently.

For that, we introduce the notion of “comparative function” for a given customer - this ultimately tells us how a customer compares the solution they’re currently using with a new one that’s proposed to them.

A solution that scores the highest among all is the one they end up converting to.

3. Selecting rational decision-making personas ⭐️

The decision-making framework defined above encompasses many different families of decision-making.

For example,

  • The Stingy - only picks the cheapest solution, i.e. alpha=(0,0,1,0)

  • The Sheep - only picks what others do, i.e. alpha=(0,1,0,0)

  • The Underanalyser - only picks the best solution, without considering the associated costs to this decision, i.e. alpha=(1,0,0,0)

In our analysis, we will focus on a specific type, which we call rational customers.

These customers are the ones who only care about ROI and think methodically. They do not care about how a solution “feels”, only about “how good of a job a solution does” + “how much would it cost using it”.

4. The efficient customer hypothesis (ECH) 💎

We’re going to introduce two important hypotheses (ECH & HECH) on the family of customers we’re going to work with. The main reasons we do this are twofold:

First, it’s a reasonable assumption to make for a rich family of markets (e.g., when one is targeting executives, CTOs, rational thinkers, hedge fund managers, etc).

Second, it opens up the door to run simulations & study startups competing with each other that adopt different strategies.

Note: In a nutshell:

  • The Efficient Customers Hypothesis (aka “ECH”): this just says every customers evaluate product gains in the same way.

  • The homogeneous efficient customers hypothesis (aka “HECH”): like ECH but in addition, it also evaluates the switching costs the same.

Finally, under the HECH, everything becomes computationally super easy to deal with!

5. Simulation frameworks 💻

We’re soon in a position where we can add simulations as a tool for us to uncover real GTO plays in startups! 🔥🧮

Some questions that are now becoming possible to answer mathematically:

  1. Are there any winning strategies for a startup entering a space where there is an incumbent with a terrible product but high switching costs? What if it’s the opposite? They got a great product, but very high switching costs?

  2. Who wins when multiple players compete, but one goes for distribution first over product?

  3. What’s the optimal resource split one should give between product & distribution?

  4. How does it work when one improves its product linearly but the distribution exponentially? What about vice versa? etc.

Man I’m so pumped for what’s next! 🔥
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